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Seminário Nacional do Benzeno (5 e 6 de dez/12) - ESTUDOS EPIDEMIOLÓGICOS E EXPOSIÇÃO AO BENZENO

Seminário Nacional do Benzeno (5 e 6 de dez/12) - ESTUDOS EPIDEMIOLÓGICOS E EXPOSIÇÃO AO BENZENO

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  • Pretty complete coverage of target group Enter study after 5 years in industry if well so short term workers excluded known to be sicker than stable workforce overwhelmingly male work force so conclusions much firmer for men than women, even so pretty big female cohort. Surveyed every 5 years collect data on health status, smoking, drinkung and job histories. At first survey data on jobs from 1975, any missing collects 1990 survey including from retirees. Quite a collection of person years now. Valuable cohort should be preserved
  • How study was set up. NHL not in excess MM in excess Leukaemia in excess very few Hodgkins (disease of young and old)
  • Qualitative exposure assessment reported in the 9th report showed an association between exposure to benzene and leukaemia. Wanted to do quantitative exposure assessment to reduce misclassification and to identify at what concentration or cumulative exposure there was a risk and possibly whether there was a threshold below which there was no risk. Had detailed job histories from individual, validated against the company records Got details from sites about technology changes Then interviewed co-workers about circumstances, product mixes time spent on various tasks etc
  • Used an exposure model used in 2 other similar pet. Ind. Studies Took measured industry data used AM to generate BE Used it in a task based model to assess exposure on an individual basis. Used EMs to adjust to local circumstances Validated BEs from literature Thought about unusual exposures not represented in BE data too rare, not done now and estimated frequency, simulated measurements and saw effect on ORs.
  • Exp y/n typical for gen community cc studies Savitz 97 exposure estimation usual limitation
  • Different jobs = different exposures Some of these not full time eg 3 of top 4 in the graph
  • Exposure within 15 years of diagnosis predicts disease but not if exposure was more than 15 years ago
  • By cell type AML elevated significantly so when 2 Acute undifferntiated leukaemias added to for ANNL
  • Pooled analysis - three previously conducted case control studies where lower benzene exposures encountered in the petroleum industry exist. Case control studies start with the disease of interest, in this case leukemia, define controls, and compare past exposure, in this case, to benzene, in cases versus controls. More exposure in cases suggests a relationship. First study: Imperial Oil workers. small study..16 leukemias no dose response. Second study: U.K., former Institute of Petroleum…difficult to interpret … dose response for some exposure metrics but not others. The third study: Australia. strong dose response for leukemia. Methodologic issues involving the baseline group and how it was defined may have affected the results. Difficult to get a clear picture of whether a dose response exists for lower exposures from each of these three studies. Pooled analysis: should provide more insight on this question. Aggregate study should provide more power, especially for leukemia subtypes, which were too few to be a focus of each individual study. Pooled data – not limited to previously done analyses like a meta-analysis. Can also standardize the data…since more cases are available, we can be more rigorous about defining the uncertainty that exists for exposure estimation and disease subtype information. Part of the strategy will be to only rely on the data with higher certainty scores, which should further enhance the accuracy of the study results. Before pooling the data, each study will be updated with cases that have occurred since the previous studies. The aggregate pooled population will consist of over 280 leukemia cases, providing more statistical power to sudy the association, which, if it exists, will likely be small and difficult to detect. The enhanced power should help the accuracy of the study, regardless of the results.
  • Before combining data we wanted to make sure that we were combining apples with apples, pommes and coxes orange pippins and pink ladies Important to involve those people who knew the data
  • MDS arises from myeloid progenitor cells (like AML), so a biological rationale for a relationship with benzene Hayes et al. (1997) reported combined (AML / MDS) related to benzene. Lack of MDS cases in unexposed prevented risk calculations…7 exposed cases Irons et al. (2010) reported a high risk between high benzene exposure and a MDS subtype Few other studies on benzene and MDS exist

Transcript

  • 1. THE HEALTH WATCH STUDY Australian petroleum industry cohort A/Prof Deborah Glass and Prof Malcolm Sim Monash Centre Occupational Environmental Health, Department of Epidemiology and Preventive Medicinewww.monash.edu.au
  • 2. Health Watch• Set up 1980• Prospective cohort study of mortality and cancer incidence• Australian petroleum industry workers – Upstream sites – Refineries – Terminals – Airports• Funded Australian Institute of Petroleum (AIP) – Large companies not small independents www.monash.edu.au 2
  • 3. Health Watch Cohort• 95% of blue collar employees interviewed – except those at sites with <10 employees• >5 years in industry• Actively followed & re-interviewed every 5 years until 2000• Surveys inc. job histories, smoking and drinking www.monash.edu.au 3
  • 4. Cohort is ageing• Over 30 years• 16,623 men and 1,375 women• 2004: 1,473 men and 34 women died – 289,275 person-years of observation in men – 19,347 person-years in women www.monash.edu.au 4
  • 5. Update to mortality and cancer incidence• Matched to national death data – end 2004• Matched to Cancer Registry data – end 2002 www.monash.edu.au 5
  • 6. Strong healthy worker effect Overall SMR Cancer SMR Cancer SIRSex (95% C.I.) (95% C.I.) (95% C.I.)Male 0.72 0.81 0.99 (0.68-0.76) (0.75-0.88) (0.94-1.04)Female 0.65 0.88 0.89 (0.45-0.91) (0.54-1.34) (0.68-1.15) All major causes of death are low www.monash.edu.au 6
  • 7. Women in Health WatchToo few women to do many analyses• 21/34 deaths were from cancer – SMR for cancer as expected• 58 cancers – SIR for cancer as expected www.monash.edu.au 7
  • 8. Mortality among men in Health WatchCause SMR (95% C.I.)Cancer (Malignant) 0.81 (0.75-0.88)Ischaemic heart disease 0.77 (0.69-0.85)Stroke 0.60 (0.46-0.77)Respiratory disease 0.73 (0.59-0.89)All diseases of the digestive system 0.57 (0.42-0.77)External Causes (accidents, violence, suicide) 0.64 (0.53-0.77)All other causes 0.55 (0.47-0.64)All causes 0.72 (0.68-0.76) www.monash.edu.au 8
  • 9. For men in Health WatchThere is no evidence of increasing cancer incidence or increasing cancer mortality with:• increasing duration of employment;• increasing time since first employment;• time period of first employment. www.monash.edu.au 9
  • 10. Cancer among men in Health Watch• Significantly excess: – Mesothelioma - 1.29 (1.13 - 1.48) – Melanoma - 1.76 (1.12 - 2.65)• Leukaemia, prostate cancer and bladder cancer are no longer in excess• Kidney cancer raised but not in significant excess in cohort or drivers www.monash.edu.au 10
  • 11. Health Watch lymphohaematopoetic (LH)cancers over time 3.7 3.2 non Hodgkin lymphoma (NHL) Multiple myeloma (MM) 2.7 LeukaemiaSIR for men 2.2 1.7 1.2 0.7 0.2 1987 1990 1993 1996 1999 2002 Year of analyses www.monash.edu.au 11
  • 12. Nested case-control questions• Is benzene exposure associated with increases in: – Leukaemia & sub-types? – Non-Hodgkin lymphoma (NHL)? – Multiple myeloma (MM)?• Is there a latent period?• Does exposure rate (peaks) matter?• Are smoking and alcohol risk factors? www.monash.edu.au 12
  • 13. Nested case-control study Health Watch Cohort (~16,000 men) 79 LH Cancer 395 Controls 5:1 age matched www.monash.edu.au 13
  • 14. Quantitative exposure assessment • Detailed job histories from cohort records – Interview – Company records • Site history • Contemporary colleague – Structured case-blind interview > tasks > products > technology www.monash.edu.au 14
  • 15. Exposure model • Exposure measurements – Company & supplementary data → Base Estimates for tasks (ppm) • Exposure modifiers – eg technology factors • Individual exposure estimates – work history + algorithm → individual exposure estimates (ppm & ppm-years) www.monash.edu.au 15
  • 16. Base estimates• 54 BEs, 49 used in study• 36 based on local data – Based on measured personal exposure to benzene – Data from Australian petroleum industry – Data from Australian sites – More than 3870 data points – Identified task/job – Routine exposure – Used AM of data www.monash.edu.au 16
  • 17. Rail car loading 3 2 1 0 Expected Normal -1 -2 -6 -4 -2 0 2 4 6 Observed Value www.monash.edu.au 17
  • 18. Laboratory worker (lubes, R&D) 3 2 1 0 Expected Normal -1 -2 -6 -5 -4 -3 -2 -1 0 1 www.monash.edu.au Observed Value 18
  • 19. Exposure metrics• Duration (years)• Intensity (average daily ppm) – Highest or longest job• Cumulative exposure (ppm-years) www.monash.edu.au 19
  • 20. Drum Filling Rail Car Loading Vehicle MaintenanceDrum Laundry & Preparation Fitting Road Tanker Loading Laboratory Tank Farm Operations Wharf & Jetty Operations Aircraft Refuelling Refinery Operations Road Tanker Driving Other Terminal Job group and Other Refinery exposure Supervision Upstream Operations Office Other Upstream 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 www.monash.edu.au Average Intensity of Exposure 20 20
  • 21. Years of employmentmean exposure period of 20 years (range 4-42) 40% 30% Cases 20% Controls 10% 0% <10 10-20 20-30 30-40 >40 www.monash.edu.au 21
  • 22. Health Watch case-control study 7 1000 Leukaemia 100 98 Odds ratio (log scale) 6 8 3 6 10 NHL/MM 1.0 0.1 <1 1-2 4-8 8-16 > 16 Cumulative Lifetime Benzene Exposure midpoint (ppm-years) www.monash.edu.au 22
  • 23. Combining two lowest exposure groups Exposure Case OR (95% CI) (ppm-years) s <2 9 1.0 2-4 8 2.9 (1.0 – 8.5) 4-8 3 1.2 (0.3 – 5.0) 8 - 16 6 3.1 (0.9 – 10.6) > 16 7 51.9 (5.6 – 477) www.monash.edu.au 23
  • 24. Leukaemia < 15 years 1000 100 34.12 10 Odds Ratio (log scale) 1 0.05Leukaemia Leukaemia > 15 yearslatency 1000 100 10 6.18 1 0.05 0 5 10 15 www.monash.edu.au Cumulative Exposure (ppm-years) 24 24
  • 25. Leukaemia exposure groups 100 50 Odds Ratio (log scale) 20 10 5 2 1 0.5 0 20 40 60 Cumulative Exposure (ppm-years) Horizontal bars indicate the range of exposure in each group www.monash.edu.au 25
  • 26. Leukaemia risk by sub-type Exposure Odds Ratio quintile ( 95%CI) Cases AML 1-3 1.00 4 4 0.00 (0.00) 0 5 8.89 (0.95-82.84) 5 ANLL 1-3 1.00 4 (AML+AUL) 4 1.11 (0.18-6.96) 2OTHER 5 8.29 (1.31-52.36) 52 ALL CML 1-3 1.00 51 hairy cell leukaemia 4 0.00 (0.00) 02 unspecified lymphocytic 5 0.78 (0.07-9.06) 1 CLL 1-3 1.00 4 4 2.25 (0.34-14.76) 2 5 7.15 (1.29-39.70) 5 www.monash.edu.au 26
  • 27. What is a peak? • Highest job? • Highest day? • Highest hour? • Highest 15 minutes? www.monash.edu.au 27
  • 28. Evidence for effects of peak exposure 1000 500 With CB/BTX casesOdds Ratio (log scale) 100 98 39 10 Without CB/BTX cases 1 0.5 0 10 20 30 40 Cumulative Exposure (ppm-years) www.monash.edu.au 28
  • 29. High exposures • 12 subjects were exposed to concentrated benzene or BTX • 5 developed leukaemia, no NHL or MM • 5/12 exposed >32 ppm-years • 4 developed leukaemia • No cases among office workers www.monash.edu.au 29 29
  • 30. Adding possible high exposures (PHEs) 16 32 64 128Cumulative Exposure with PHEsadded log scale (ppm/years) 8 4 2 1 1 2 4 8 16 32 64 128 Cumulative Exposure log scale (ppm/years) www.monash.edu.au 30
  • 31. Odds ratios reduced Benzene Number of Cumulative exposure exposure cases and PHEs (ppm-years) OR (95% CI) ≤2 9 1.0 >2-4 8 3.1 (1.0 - 9.3) >4-8 3 1.2 (0.3 - 5.2) >8-16 6 2.7 (0.7 - 10.1) >16 7 7.8 (2.3 - 25.9) www.monash.edu.au 31
  • 32. Summary of case-control results• NHL MM - not associated with benzene exposure• Leukaemia - strongly positive – ANNL & CLL ~ positive• Significant excess risk at >16 ppm-years – Cum exp and intensity too close to separate• Latency period ≈ 10-15 years• Effect of “peaks” – some evidence• No association with smoking or alcohol www.monash.edu.au 32 32
  • 33. THE POOLED PETROLEUMINDUSTRY CASE-CONTROL STUDYwww.monash.edu.au
  • 34. Pooled study (published online JNCI 30/10/12)• 3 case-control studies (Canadian, UK, Australian) nested in petroleum industry cohorts• Each updated with new cases and pooled • more power for leukaemia subtypes • use WHO classification of LH cancers• Similar design, case and control identification, exposure assessment and analytical methods www.monash.edu.au 34
  • 35. Aims of the studyTo investigate the relationship betweenexposure to benzene and risk of leukaemia – Evaluate dose-response overall – Evaluate by WHO subtype – Include leukaemias, MDS and MPD – Explore influence on dose-response relationships of study, site type, job, lag/latency, exposure metrics www.monash.edu.au 35
  • 36. Pooling three nested case control studies U.K.2 Canada1 Australia3 inconsistent dose no consistent strong dose response, response, depending onRefs: dose response, but small study subgroups and different for ANNL & CLL exposure metrics1. Schnatter et al. 1996 53:773-781. based on 31 LH cancers based on 90 leukaemias based on 79 LH cancers2. Rushton et al. 1997 54: 152-166. before pooling data  update studies3. Glass et al. 200314: 569-577. 60 LH cancers 193 LH cancers 117 LH cancers Incl. 5 MDS Incl. 11 MDS Incl. 13 MDS 370 LH cancers www.monash.edu.au 36
  • 37. Pooled study steps• Ethical approvals, identify new cases & controls, obtain work histories, carry out exposure assessment• Ensure consistency of disease classification • Certainty of diagnosis• Assess consistency of exposure assessments – Development of common job groups – Development of peak and skin exposure metrics – Certainty of exposure – Comparability of background exposure – Rationalization of differences across studies• Statistical Analyses www.monash.edu.au 37
  • 38. Checked lymphohaematopoietic (LH) cancer classificationTraditional Paradigm: Anatomy• LEUKAEMIAS (in peripheral blood)• LYMPHOMAS (in lymph system)New Paradigm: Cell of Origin• MYELOID tumours – Myeloproliferative Disease (MPD) – Myelodysplastic Syndrome (MDS) – Acute Myeloid Leukaemias (AML)• LYMPHOID tumours – B-cells – T-cells (leukaemias and lymphomas) www.monash.edu.au 38
  • 39. Quantitative exposure assessment • Individual job histories • Site histories • Exposure of new cases and controls assessed by original study method • Exposure intensity (ppm) for each job • Confidence score for each estimate – L, M, H www.monash.edu.au 39
  • 40. Pooled study exposure assessment• Team of study hygienists and external hygienist• Peak exposure metric – Prob. >3ppm for 15-60 mins at least weekly• Skin exposure metric – 0, L, M, H prob of at least weekly skin exposure• Prepared common list of job categories – Allocated each job for each individual www.monash.edu.au 40
  • 41. Exposure assessment rationalisation• Compared job estimates between studies• Estimates in each job category by era – pre 1945, 1945-1960, 1960s & 1970s and 1980+ – AM, GM, n, max & min• If mean were same (within 10%)- no change• If different – Checked job/technology/products/conditions – If no apparent local explanation, adjust• Some job cats. had no other study comparison – Different industry sector or period www.monash.edu.au 41
  • 42. Study exposure estimates Little change from original estimates Little difference between original and revised www.monash.edu.au 42
  • 43. Statistical analyses• Risk as ORs by exposure : – cumulative benzene (ppm-years) > ppm-years within relevant window (lag/latency) – average & maximum intensity (ppm) – peaks & skin• Job category, industry sector• Sensitivity analyses: study, job confidence, exposure confidence www.monash.edu.au 43
  • 44. www.monash.edu.au 44
  • 45. MDS, cumulative benzene exposure and certainty of diagnosis100 10 11.6Odds Ratios 4.33 3.47 1.73 1 All Subjects More Certain Diagnoses0.1 >0.348 and >2.93 >0.348 and >2.93 <2.93 Cumulative Exposure (ppm-years) <2.93 www.monash.edu.au 45
  • 46. Pooled analysis MDS cases and controls Current exposure zone Suggests MDS cases over-represented at approx 1+ ppm www.monash.edu.au 46
  • 47. CONCAWE study findings• MDS associated with low benzene exposure• MDS may be a more sensitive outcome than AML• AML: several ORs were >1, few statistically sig. • perhaps higher benzene exp. for sig. risks of AML • some cases formerly classified as AML were MDS• CML: several ORs >1, but no clear exposure response pattern• CLL and MPD: no strong relationship www.monash.edu.au 47
  • 48. Putting the evidence together• Epidemiology assesses risk for the group Can it be applied to other groups?• Risk estimates from single studies wobbly• Pooled data or meta-analyses needed for conclusions about causation www.monash.edu.au 48
  • 49. Population risk vs individual risk• Attribution at a population level – Benzene exposure increases the incidence of leukaemia• Attribution for an individual – Benzene exposure caused leukaemia in this person www.monash.edu.au 49
  • 50. Acknowledgements• Australian Institute of Petroleum• Institute of Petroleum (UK)• American Petroleum Institute• Conservation for Clean Air and Water Europe (CONCAWE)• Aromatic Producers Association• Energy Institute• Canadian Petroleum Products Institute www.monash.edu.au 50